The nature of statistical learning theory
The nature of statistical learning theory
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
A new discriminative kernel from probabilistic models
Neural Computation
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Asymptotic properties of the Fisher kernel
Neural Computation
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Supervised Neural Gas with General Similarity Measure
Neural Processing Letters
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolving fisher kernels for biological sequence classification
Evolutionary Computation
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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Splice sites are locations in DNA which separate protein-coding regions (exons) from noncoding regions (introns). Accurate splice site detectors thus form important components of computational gene finders. We pose splice site recognition as a classification problem with the classifier learnt from a labeled data set consisting of only local information around the potential splice site. Note that finding the correct position of splice sites without using global information is a rather hard task. We analyze the genomes of the nematode Caenorhabditis elegans and of humans using specially designed support vector kernels. One of the kernels is adapted from our previous work on detecting translation initiation sites in vertebrates and another uses an extension to the well-known Fisher-kernel. We find excellent performance on both data sets.